| paper | d4-conf-atal-2013-0199 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | medium |
| authors would recognise it | unclear |
Theorem 2 qualifies as a computational anchor under the prompt's inclusive rule for algorithmic performance guarantees: it bounds the redistribution ratio of the specified rule. But the positive case states no continuous problem for that result, and the opponent explains why replacing an individual report with a change in fluid type mass changes the incentive counterfactual. No continuous computational question survives as stated.
fails bit b-changes — the continuum limit changes the question
Theorem 2's last-period redistribution guarantee is the only plausible computational anchor; the DSIC characterization, budget-balance proofs, departure-time rule, and simulations have no stated continuous computational counterpart.
This paper contains no named computational complexity result to anchor a continuous mirror. Lemma 1 characterizes DSIC payments; Theorems 1 and 3 prove weak budget balance for two redistribution rules; Theorem 2 gives a worst-case performance guarantee. None classifies an underlying problem as tractable or hard, and the numerical simulations are not such a result.
A continuum of agents with types \((a,d,v)\) and per-capita supply could be a plausible research model, but the paper gives no qualifying anchor for it. Presenting it as the requested mirror would overstate the connection.
The proponent’s only anchor is that the paper has no named computational complexity result, and that is its strongest point. Lemma 1 characterizes payments that preserve DSIC; Theorems 1 and 3 establish weak budget balance; Theorem 2 bounds a redistribution ratio. None studies the running time or complexity of a computational problem. The simulations measure how much revenue is redistributed, not how difficult it is to compute a mechanism. So there is no result here of the kind ChoCo is set up to mirror.
A plausible high-multiplicity setting does exist: many electric-vehicle sessions or cloud jobs could share the same arrival window, departure window, and value. But the paper’s key constraints are about an individual’s counterfactual report. Its rebates use \(\theta_{-i}\), its critical payments depend on whether one agent changes the allocation, and Lemma 5 relies on one agent displacing one winner. In an atomless population, removing one agent leaves the type distribution unchanged. A fluid model can preserve a tagged, infinitesimal agent’s incentives, or study deviations by a positive mass, but those choices lead to different incentive questions; neither directly carries over the paper’s single-agent results.
For the last-period rule, the most natural fluid question would be about the limiting rebate ratio. Yet the paper already shows that this ratio approaches one as \(n\) grows for fixed \(T\). A fluid formulation would largely restate that asymptotic result. A fluid version of the departure-time rule could give a useful analysis of how rebates vary with overlap and value distributions, but that would extend the paper’s mechanism-design and simulation agenda; it would not supply the computational-complexity mirror ChoCo seeks.
So the negative case is strong for this paper as a source of ChoCo anchors: it has no qualifying computational result, and its central claims depend on individual deviations. The universal claim is weaker. One could formulate a new computational problem about optimizing redistribution over a fluid online market, but that would be a new research direction rather than a continuous mirror of a result in this paper.
The adversarial triple: the proponent anchors on up to three named results; the opponent sees that case and must defeat every anchor; the judge decides which case convinced it. These are the pipeline’s own outputs, generated by tools/triple_run.py — no human edited them. The paper’s own text is not reproduced here beyond the quoted statement above.